Researchers working on FrameNet Brazil, a machine-readable lexicon based on the original English FrameNet housed at ICSI, are helping build a trilingual dictionary – in English, Spanish, and Portuguese – in preparation for the FIFA World Cup soccer championships, which will be held in Brazil next year. The dictionary will have an emphasis on words and phrases related to tourism and soccer. FrameNet Brazil, or FN-Br, was established in 2007 and now comprises seven researchers and more than two dozen students, from undergraduates to postdocs.
Speech researchers at ICSI are hard at work on the Swordfish project, an ambitious effort to rapidly generate keyword search systems in a new language with modest resources. The ultimate goal would be to develop a system for a new language in a week using only a small amount of labeled training data (as little as 10 hours - most ASR systems are trained on hundreds or thousands of hours of labeled data).
Audio and Multimedia researchers at ICSI are starting to work on a new big data project that aims to provide the scalability of diverse parallel processing at the productivity level of high-level languages.
The new SMASH project (Scalable Multimedia content AnalysiS in a High-level language) is a collaboration between researchers at ICSI and UCB and is funded by a grant from the National Science Foundation (NSF).
The National Center for Supercomputing Applications (NCSA) at the University of Illinois faces unique security challenges as it must both maintain an open academic environment accessible to researchers around the world and protect some of the most valuable IT assets in the nation, a point brought home by the recent inauguration of the Blue Waters petascale computing system.
Digital images have become ubiquitous companions of our everyday life. At the same time, the very nature of digital data puts into question many of the positive aspects that we usually associate with digital images. Digital data can be manipulated easily. Powerful editing software, which allows even inexperienced users to conveniently process digital images in innumerable ways, raises questions regarding the authenticity of digital images.
Using techniques from the field of robotics, Vision Group researchers and their colleagues have developed a method for detecting objects in images that intelligently selects which object detectors to use and which to ignore in order to complete a task within given time constraints. The paper was presented in December at the Neural Information Processing Systems Conference. It’s by Vision Group researcher Sergey Karayev and group leader Trevor Darrell as well as Tobias Baumgartner of RWTH Aachen University and Mario Fritz of MPI for Informatics, who has worked at ICSI as a postdoc.
Financial records of three vendors that sell unauthorized and counterfeit pharmaceuticals over the Internet show, among other things, that they rely on a relatively small number of affiliate advertisers to drive traffic to their sites. An analysis of the records by Networking researchers and their collaborators gives a rare insider’s view of the finances of illicit online activity.
Researchers have developed a method to identify rooms through audio recordings which were captured in them. Nils Peters, a DAAD-funded postdoctoral fellow, and Speech Group researchers Gerald Friedland and Howard Lei used audio recordings from seven different rooms – a bedroom, library, studio, two churches, great hall, and classroom – to develop an acoustic profile for each. These profiles were based on audio features that are frequently used by speech systems to automatically recognize words.
Spammers who posted almost half a million Twitter messages in order to silence debate over Russia’s election in December likely purchased fraudulent accounts in bulk and posted the tweets from botnets, groups of malware-infected computers under the command of a single person. According to Networking Group researchers, the campaign took advantage of an underground economy based on spam, a phenomenon that researchers are studying in an attempt to improve methods of eliminating spam.
Computer vision techniques have trouble recognizing subcategories of objects (for example, a vehicle’s model type or a bird’s species). A new method developed by ICSI researchers improves automatic recognition of subcategories by first warping small areas of photos to account for differences in pose and angle, and then grouping the areas according to their similarities.
Researchers working on 
Guest post by Adam Slagell, NCSA and the Bro team
Guest post by Matthias Kirchner
Financial records of three vendors that sell unauthorized and counterfeit pharmaceuticals over the Internet show, among other things, that they rely on a relatively small number of affiliate advertisers to drive traffic to their sites. An analysis of the records by Networking researchers and their collaborators gives a rare insider’s view of the finances of illicit online activity.
Researchers have developed a method to identify rooms through audio recordings which were captured in them. Nils Peters, a DAAD-funded postdoctoral fellow, and Speech Group researchers Gerald Friedland and Howard Lei used audio recordings from seven different rooms – a bedroom, library, studio, two churches, great hall, and classroom – to develop an acoustic profile for each. These profiles were based on audio features that are frequently used by speech systems to automatically recognize words.
Spammers who posted almost half a million Twitter messages in order to silence debate over Russia’s election in December likely purchased fraudulent accounts in bulk and posted the tweets from botnets, groups of malware-infected computers under the command of a single person. According to Networking Group researchers, the campaign took advantage of an underground economy based on spam, a phenomenon that researchers are studying in an attempt to improve methods of eliminating spam.
Computer vision techniques have trouble recognizing subcategories of objects (for example, a vehicle’s model type or a bird’s species). A new method developed by ICSI researchers improves automatic recognition of subcategories by first warping small areas of photos to account for differences in pose and angle, and then grouping the areas according to their similarities.